SCZ - Brain Putamen basal ganglia

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Last updated: 2022-03-03

Checks: 5 2

Knit directory: cTWAS_analysis/

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Weight QC

#number of imputed weights
nrow(qclist_all)

[1] 11274

#number of imputed weights by chromosome
table(qclist_all$chr)

   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
1123  795  659  440  534  593  558  410  421  464  669  651  228  384  383  516 
  17   18   19   20   21   22 
 678  179  848  340  123  278 

#number of imputed weights without missing variants
sum(qclist_all$nmiss==0)

[1] 8904

#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)

[1] 0.7898

Check convergence of parameters

Past versions of unnamed-chunk-5-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior)

     gene       snp 
0.0123138 0.0002506 

#estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var)

  gene    snp 
10.662  8.503 

#report sample size
print(sample_size)

[1] 82315

#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)

[1]   11274 7573890

#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve)

   gene     snp 
0.01798 0.19610 

#compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))

[1] 0.1204 1.4388

Genes with highest PIPs

Past versions of unnamed-chunk-7-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

        genename region_tag susie_pip     mu2       PVE      z num_eqtl
3448       CRHR1      17_27    0.9977 3537.44 0.0428738  3.362        1
10867     ZNF823      19_10    0.9813   29.49 0.0003516  5.455        1
4092       FEZF1       7_74    0.9787   28.51 0.0003390 -5.314        1
11990 AC012074.2       2_15    0.9012   21.92 0.0002399  4.623        1
8791       GNG12       1_42    0.8876   22.45 0.0002421  4.526        2
3043       SF3B1      2_117    0.8595   43.81 0.0004574  6.725        1
11945  HIST1H2BN       6_21    0.7887   91.05 0.0008723 10.773        1
6321       PLBD2      12_68    0.7749   20.26 0.0001907  3.986        1
8798        FUT9       6_65    0.7434   29.72 0.0002684  5.427        1
7435    SERPINI1      3_103    0.6967   20.40 0.0001726 -4.038        1
433       ARID1B      6_102    0.6827   22.81 0.0001892 -3.907        1
13621  LINC02033       3_27    0.6750   42.14 0.0003455 -6.688        1
11497      AS3MT      10_66    0.6629   47.24 0.0003805  8.510        2
4444       REEP2       5_82    0.6544   27.96 0.0002223  5.204        1
10737      PCBP2      12_33    0.6333   22.13 0.0001703  4.202        1
11110 LIN28B-AS1       6_70    0.6276   23.11 0.0001762 -4.630        2
3935        KLC1      14_54    0.6130   41.31 0.0003076  7.069        1
11329      ITSN1      21_14    0.6071   24.37 0.0001798  3.885        1
5721      CEP170      1_128    0.5858   24.35 0.0001733 -4.678        1
905        NT5C2      10_66    0.5494   40.33 0.0002691 -8.066        1

Genes with largest effect sizes

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

         genename region_tag susie_pip     mu2       PVE        z num_eqtl
3448        CRHR1      17_27 9.977e-01 3537.44 4.287e-02  3.36232        1
10167      ARL17A      17_27 0.000e+00 3039.45 0.000e+00 -2.78513        2
6964     ARHGAP27      17_27 0.000e+00  338.09 0.000e+00  0.34014        1
12064    HLA-DQA2       6_26 1.865e-14  251.01 5.688e-17  0.21639        1
10691    HLA-DQA1       6_26 2.209e-14  201.77 5.415e-17  3.44601        1
12119      LY6G5B       6_26 7.245e-09  185.79 1.635e-11 -7.00014        1
10035       FMNL1      17_27 0.000e+00  140.29 0.000e+00 -0.66376        1
11190        MSH5       6_26 1.045e-04  127.66 1.621e-07  7.59028        2
4897         NMT1      17_27 0.000e+00  119.81 0.000e+00  2.85333        1
8857        DCAKD      17_27 0.000e+00  115.79 0.000e+00 -2.99967        1
9689        ACBD4      17_27 0.000e+00  105.48 0.000e+00  0.12846        2
11639         C4B       6_26 5.842e-13  102.32 7.262e-16 -4.92818        1
11945   HIST1H2BN       6_21 7.887e-01   91.05 8.723e-04 10.77288        1
9507     HLA-DQB1       6_26 6.661e-15   85.64 6.930e-18  4.33946        1
10958    HLA-DRB5       6_26 4.996e-15   75.21 4.565e-18  2.07566        1
10276      HEXIM1      17_27 0.000e+00   73.25 0.000e+00 -2.84515        1
2412        GOSR2      17_27 0.000e+00   72.14 0.000e+00 -2.50963        1
13230 RP1-86C11.7       6_21 1.175e-01   71.63 1.022e-04  9.03322        1
11877     CYP21A2       6_26 1.862e-13   63.59 1.438e-16 -0.01504        1
10244      BTN3A2       6_20 1.530e-02   56.53 1.051e-05  8.19734        3

Genes with highest PVE

        genename region_tag susie_pip     mu2       PVE      z num_eqtl
3448       CRHR1      17_27    0.9977 3537.44 0.0428738  3.362        1
11945  HIST1H2BN       6_21    0.7887   91.05 0.0008723 10.773        1
3043       SF3B1      2_117    0.8595   43.81 0.0004574  6.725        1
11497      AS3MT      10_66    0.6629   47.24 0.0003805  8.510        2
10867     ZNF823      19_10    0.9813   29.49 0.0003516  5.455        1
13621  LINC02033       3_27    0.6750   42.14 0.0003455 -6.688        1
4092       FEZF1       7_74    0.9787   28.51 0.0003390 -5.314        1
3935        KLC1      14_54    0.6130   41.31 0.0003076  7.069        1
905        NT5C2      10_66    0.5494   40.33 0.0002691 -8.066        1
8798        FUT9       6_65    0.7434   29.72 0.0002684  5.427        1
2590         MDK      11_28    0.5312   38.43 0.0002480 -6.357        1
8791       GNG12       1_42    0.8876   22.45 0.0002421  4.526        2
11990 AC012074.2       2_15    0.9012   21.92 0.0002399  4.623        1
4444       REEP2       5_82    0.6544   27.96 0.0002223  5.204        1
6321       PLBD2      12_68    0.7749   20.26 0.0001907  3.986        1
433       ARID1B      6_102    0.6827   22.81 0.0001892 -3.907        1
11329      ITSN1      21_14    0.6071   24.37 0.0001798  3.885        1
11110 LIN28B-AS1       6_70    0.6276   23.11 0.0001762 -4.630        2
5721      CEP170      1_128    0.5858   24.35 0.0001733 -4.678        1
7435    SERPINI1      3_103    0.6967   20.40 0.0001726 -4.038        1

Genes with largest z scores

         genename region_tag susie_pip    mu2       PVE      z num_eqtl
11945   HIST1H2BN       6_21 7.887e-01  91.05 8.723e-04 10.773        1
13230 RP1-86C11.7       6_21 1.175e-01  71.63 1.022e-04  9.033        1
11497       AS3MT      10_66 6.629e-01  47.24 3.805e-04  8.510        2
10244      BTN3A2       6_20 1.530e-02  56.53 1.051e-05  8.197        3
905         NT5C2      10_66 5.494e-01  40.33 2.691e-04 -8.066        1
6164        CNNM2      10_66 7.538e-02  34.34 3.145e-05 -7.691        1
11190        MSH5       6_26 1.045e-04 127.66 1.621e-07  7.590        2
3935         KLC1      14_54 6.130e-01  41.31 3.076e-04  7.069        1
12119      LY6G5B       6_26 7.245e-09 185.79 1.635e-11 -7.000        1
10392     ZSCAN23       6_22 1.162e-01  47.98 6.775e-05 -6.789        2
3043        SF3B1      2_117 8.595e-01  43.81 4.574e-04  6.725        1
10545     ZKSCAN3       6_22 2.269e-02  33.66 9.278e-06  6.709        1
13621   LINC02033       3_27 6.750e-01  42.14 3.455e-04 -6.688        1
10732     ZSCAN26       6_22 1.608e-02  37.56 7.336e-06  6.658        3
6302        ABCB9      12_75 7.651e-03  38.72 3.599e-06  6.404        1
2590          MDK      11_28 5.312e-01  38.43 2.480e-04 -6.357        1
5872       CCDC39      3_111 2.934e-01  38.48 1.372e-04 -6.338        1
2929         FXR1      3_111 1.977e-01  37.68 9.050e-05  6.308        1
1212     PPP1R13B      14_54 9.198e-02  42.90 4.793e-05  6.297        1
13228   U91328.19       6_20 5.982e-02  41.81 3.038e-05 -6.254        1

Comparing z scores and PIPs

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Version	Author	Date
ff6403a	sq-96	2022-02-27

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Version	Author	Date
ff6403a	sq-96	2022-02-27

#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)

[1] 0.006741

GO enrichment analysis for genes with PIP>0.5

#number of genes for gene set enrichment
length(genes)

[1] 27

Uploading data to Enrichr... Done.
  Querying GO_Biological_Process_2021... Done.
  Querying GO_Cellular_Component_2021... Done.
  Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"

Past versions of unnamed-chunk-15-1.png

Version	Author	Date
2bd17d9	sq-96	2022-03-03

                                                                        Term
1                    regulation of leukocyte cell-cell adhesion (GO:1903037)
2 regulation of leukocyte adhesion to vascular endothelial cell (GO:1904994)
3          positive regulation of neuron projection development (GO:0010976)
4           positive regulation of cell projection organization (GO:0031346)
  Overlap Adjusted.P.value             Genes
1    2/12          0.01811          FUT9;MDK
2    2/13          0.01811          FUT9;MDK
3    3/88          0.01985 FUT9;MDK;SERPINI1
4   3/117          0.03438 FUT9;MDK;SERPINI1
[1] "GO_Cellular_Component_2021"

Past versions of unnamed-chunk-15-2.png

Version	Author	Date
2bd17d9	sq-96	2022-03-03

                                              Term Overlap Adjusted.P.value
1 protein phosphatase type 2A complex (GO:0000159)    2/17         0.008722
         Genes
1 PTPA;PPP2R5B
[1] "GO_Molecular_Function_2021"

Past versions of unnamed-chunk-15-3.png

Version	Author	Date
2bd17d9	sq-96	2022-03-03

                                                 Term Overlap Adjusted.P.value
1 protein phosphatase activator activity (GO:0072542)    2/13         0.007189
         Genes
1 PTPA;PPP2R5B

DisGeNET enrichment analysis for genes with PIP>0.5

                                                  Description     FDR Ratio
3                                           Anxiety Disorders 0.02009  2/13
60                                   Anxiety States, Neurotic 0.02009  2/13
94                                 Anxiety neurosis (finding) 0.02009  2/13
103 Familial encephalopathy with neuroserpin inclusion bodies 0.02009  1/13
113                SPASTIC PARAPLEGIA 72, AUTOSOMAL RECESSIVE 0.02009  1/13
114                 SPASTIC PARAPLEGIA 72, AUTOSOMAL DOMINANT 0.02009  1/13
116                SPASTIC PARAPLEGIA 45, AUTOSOMAL RECESSIVE 0.02009  1/13
117                                     CONE-ROD DYSTROPHY 20 0.02009  1/13
118  HYPOGONADOTROPIC HYPOGONADISM 22 WITH OR WITHOUT ANOSMIA 0.02009  1/13
28                        Neoplasms, Glandular and Epithelial 0.02125  1/13
    BgRatio
3   44/9703
60  44/9703
94  44/9703
103  1/9703
113  1/9703
114  1/9703
116  1/9703
117  1/9703
118  1/9703
28   2/9703

WebGestalt enrichment analysis for genes with PIP>0.5

Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...

Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet, minNum =
minNum, : No significant gene set is identified based on FDR 0.05!

NULL

PIP Manhattan Plot

Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

Past versions of unnamed-chunk-18-1.png

Version	Author	Date
2bd17d9	sq-96	2022-03-03

Sensitivity, specificity and precision for silver standard genes

#number of genes in known annotations
print(length(known_annotations))

[1] 130

#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))

[1] 63

#significance threshold for TWAS
print(sig_thresh)

[1] 4.59

#number of ctwas genes
length(ctwas_genes)

[1] 6

#number of TWAS genes
length(twas_genes)

[1] 76

#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols]

     genename region_tag susie_pip     mu2       PVE     z num_eqtl
8791    GNG12       1_42    0.8876   22.45 0.0002421 4.526        2
3448    CRHR1      17_27    0.9977 3537.44 0.0428738 3.362        1

#sensitivity / recall
print(sensitivity)

  ctwas    TWAS 
0.02308 0.06923 

#specificity
print(specificity)

 ctwas   TWAS 
0.9997 0.9940 

#precision / PPV
print(precision)

 ctwas   TWAS 
0.5000 0.1184 

Past versions of unnamed-chunk-21-1.png

Version	Author	Date
2bd17d9	sq-96	2022-03-03

cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes

#number of genes in known annotations (with imputed expression)
print(length(known_annotations))

[1] 63

#number of bystander genes (with imputed expression)
print(length(unrelated_genes))

[1] 830

#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$genename %in% c(known_annotations, unrelated_genes),]

#assign ctwas and TWAS genes
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>0.8]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>sig_thresh]

#significance threshold for TWAS
print(sig_thresh)

[1] 4.59

#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)

[1] 3

#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)

[1] 20

#sensitivity / recall
sensitivity

  ctwas    TWAS 
0.04762 0.14286 

#specificity / (1 - False Positive Rate)
specificity

 ctwas   TWAS 
1.0000 0.9867 

#precision / PPV / (1 - False Discovery Rate)
precision

ctwas  TWAS 
 1.00  0.45 

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Version	Author	Date
2bd17d9	sq-96	2022-03-03
3f6d410	sq-96	2022-03-02

Past versions of unnamed-chunk-26-1.png

Version	Author	Date
2bd17d9	sq-96	2022-03-03

pip_range <- (0:1000)/1000
sensitivity <- rep(NA, length(pip_range))
specificity <- rep(NA, length(pip_range))

for (index in 1:length(pip_range)){
  pip <- pip_range[index]
  ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>=pip]
  sensitivity[index] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
  specificity[index] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
}

plot(1-specificity, sensitivity, type="l", xlim=c(0,1), ylim=c(0,1), main="", xlab="1 - Specificity", ylab="Sensitivity")
title(expression("ROC Curve for cTWAS (black) and TWAS (" * phantom("red") * ")"))
title(expression(phantom("ROC Curve for cTWAS (black) and TWAS (") * "red" * phantom(")")), col.main="red")

sig_thresh_range <- seq(from=0, to=max(abs(ctwas_gene_res_subset$z)), length.out=length(pip_range))

for (index in 1:length(sig_thresh_range)){
  sig_thresh_plot <- sig_thresh_range[index]
  twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>=sig_thresh_plot]
  sensitivity[index] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
  specificity[index] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
}

lines(1-specificity, sensitivity, xlim=c(0,1), ylim=c(0,1), col="red", lty=1)

abline(a=0,b=1,lty=3)

#add previously computed points from the analysis
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>0.8]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>sig_thresh]

points(1-specificity_plot["ctwas"], sensitivity_plot["ctwas"], pch=21, bg="black")
points(1-specificity_plot["TWAS"], sensitivity_plot["TWAS"], pch=21, bg="red")

Undetected silver standard genes have low TWAS z-scores or stronger signal from nearby variants

#table of outcomes for silver standard genes
-sort(-table(silver_standard_case))

silver_standard_case
          Not Imputed Insignificant z-score         Nearby SNP(s) 
                   67                    53                     7 
 Detected (PIP > 0.8) 
                    3 

#show inconclusive genes
silver_standard_case[silver_standard_case=="Inconclusive"]

named character(0)

Past versions of unnamed-chunk-31-1.png

Version	Author	Date
2bd17d9	sq-96	2022-03-03
35c93ea	sq-96	2022-03-02

Locus plot for three silver standard genes that cTWAS identifies

Locus 2_117: The output of cTWAS is very clear. Only gene SF3B1 has signal.

locus_plot("2_117", label="TWAS")

Past versions of unnamed-chunk-32-1.png

Version	Author	Date
2bd17d9	sq-96	2022-03-03
35c93ea	sq-96	2022-03-02

Locus 17_27: In TWAS, no SNP or gene passes the threshold. But cTWAS is able to identify gene CRHR1 with high PIP.

locus_plot("17_27", label="TWAS")

Past versions of unnamed-chunk-33-1.png

Version	Author	Date
2bd17d9	sq-96	2022-03-03
35c93ea	sq-96	2022-03-02

Locus 19_10: Both TWAS and cTWAS work well in this locus.

locus_plot("19_10", label="TWAS")

Past versions of unnamed-chunk-34-1.png

Version	Author	Date
2bd17d9	sq-96	2022-03-03
154ca16	sq-96	2022-03-02
35c93ea	sq-96	2022-03-02

Some know SCZ risk genes that cTWAS does not find

Locus 15_42: cTWAS assign higher PIP to the SNP rs8032315 rather than the gene FURIN. This SNP is both eQTL and sQTL of FES in Nerve-Tibial and sQTL of FURIN in Nerve-Tibial

locus_plot("15_42", label="TWAS")

Past versions of unnamed-chunk-35-1.png

Version	Author	Date
2bd17d9	sq-96	2022-03-03
48b0ed5	sq-96	2022-03-02

Locus 19_34: Gene IRF3 is around the threshold in TWAS. There are other two genes of similar value. In ctwas, all of them get intermediate PIPs.

locus_plot5("19_34", focus="IRF3")

Past versions of unnamed-chunk-36-1.png

Version	Author	Date
2bd17d9	sq-96	2022-03-03
48b0ed5	sq-96	2022-03-02

Locus 1_6: The neurodevelopmental disorder gene RERE has insignificant z-score

locus_plot5("1_6", focus="RERE")

Past versions of unnamed-chunk-37-1.png

Version	Author	Date
2bd17d9	sq-96	2022-03-03
48b0ed5	sq-96	2022-03-02

Locus 17_37: ACE encoding angiotensin converting enzyme, the target of a major class of anti-hypertensive drugs (schizophrenia under expression), has insignificant z-score

locus_plot5("17_37", focus="ACE")

Past versions of unnamed-chunk-38-1.png

Version	Author	Date
2bd17d9	sq-96	2022-03-03

Locus 3_27: DCLK3 encoding a neuroprotective kinase (schizophrenia under-expression), has insignificant z-score. Both cTWAS and TWAS identify another gene LINC02033, which might be of interest.

locus_plot("3_27", label="TWAS")

Session information

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] readxl_1.3.1      forcats_0.5.1     stringr_1.4.0     dplyr_1.0.7      
 [5] purrr_0.3.4       readr_2.1.1       tidyr_1.1.4       tidyverse_1.3.1  
 [9] tibble_3.1.6      WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0      
[13] cowplot_1.0.0     ggplot2_3.3.5     workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] fs_1.5.2          lubridate_1.8.0   bit64_4.0.5       doParallel_1.0.17
 [5] httr_1.4.2        rprojroot_2.0.2   tools_3.6.1       backports_1.4.1  
 [9] doRNG_1.8.2       utf8_1.2.2        R6_2.5.1          vipor_0.4.5      
[13] DBI_1.1.2         colorspace_2.0-2  withr_2.4.3       ggrastr_1.0.1    
[17] tidyselect_1.1.1  processx_3.5.2    bit_4.0.4         curl_4.3.2       
[21] compiler_3.6.1    git2r_0.26.1      rvest_1.0.2       cli_3.1.0        
[25] Cairo_1.5-12.2    xml2_1.3.3        labeling_0.4.2    scales_1.1.1     
[29] callr_3.7.0       apcluster_1.4.8   digest_0.6.29     rmarkdown_2.11   
[33] svglite_1.2.2     pkgconfig_2.0.3   htmltools_0.5.2   dbplyr_2.1.1     
[37] fastmap_1.1.0     highr_0.9         rlang_1.0.1       rstudioapi_0.13  
[41] RSQLite_2.2.8     jquerylib_0.1.4   farver_2.1.0      generics_0.1.1   
[45] jsonlite_1.7.2    vroom_1.5.7       magrittr_2.0.2    Matrix_1.2-18    
[49] ggbeeswarm_0.6.0  Rcpp_1.0.8        munsell_0.5.0     fansi_1.0.2      
[53] gdtools_0.1.9     lifecycle_1.0.1   stringi_1.7.6     whisker_0.3-2    
[57] yaml_2.2.1        plyr_1.8.6        grid_3.6.1        blob_1.2.2       
[61] ggrepel_0.9.1     parallel_3.6.1    promises_1.0.1    crayon_1.5.0     
[65] lattice_0.20-38   haven_2.4.3       hms_1.1.1         knitr_1.36       
[69] ps_1.6.0          pillar_1.6.4      igraph_1.2.10     rjson_0.2.20     
[73] rngtools_1.5.2    reshape2_1.4.4    codetools_0.2-16  reprex_2.0.1     
[77] glue_1.6.2        evaluate_0.14     getPass_0.2-2     modelr_0.1.8     
[81] data.table_1.14.2 vctrs_0.3.8       tzdb_0.2.0        httpuv_1.5.1     
[85] foreach_1.5.2     cellranger_1.1.0  gtable_0.3.0      assertthat_0.2.1 
[89] cachem_1.0.6      xfun_0.29         broom_0.7.10      later_0.8.0      
[93] iterators_1.0.14  beeswarm_0.2.3    memoise_2.0.1     ellipsis_0.3.2